Effect of Signal Filtering on Metaheuristic-Based Structural Parameter Identification in Shear Building Models
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This study evaluates the effectiveness of three metaheuristic algorithms—Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO)—for identifying lateral interstory stiffness and the modal damping ratio in two-dimensional shear building models. The main objective is to estimate these parameters using time-domain displacement, velocity, and acceleration data, assuming known floor masses and unknown input excitation that primarily excites translational vibration modes. Three structural configurations with 2, 3, and 5 stories are analyzed to assess the scalability and robustness of each algorithm. To assess the effect of signal filtering on the performance of the algorithms, white noise is added to the synthetic response data at six levels ranging from 0% to 5% of the root mean square (RMS) amplitude. A sixth-order Butterworth filter is applied to evaluate the effect of signal preprocessing, and results obtained with and without filtering are compared. The results show that all three algorithms achieve acceptable levels of accuracy, even under noisy conditions. Filtering consistently improves identification accuracy, especially in high-noise conditions. In the most challenging case (5% noise, 5-story model), the average identification errors were 5.042% for GA, 5.106% for DE, and 5.035% for PSO. The findings underscore the practical value of integrating signal filtering with metaheuristic optimization for robust structural system identification in noise-contaminated environments. To account for the random nature of the algorithms, all results reported correspond to the average of 10 independent runs per identification scenario to ensure reliable performance evaluation.
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